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Found 27 papers, 4 papers with code
Logic of subjective probability
In this paper I discuss both syntax and semantics of subjective probability.
Conformal testing: binary case with Markov alternatives
We continue study of conformal testing in binary model situations.
Adaptive calibration for binary classification
This note proposes a way of making probability forecasting rules less sensitive to changes in data distribution, concentrating on the simple case of binary classification.
Enhancement of prediction algorithms by betting
This note proposes a procedure for enhancing the quality of probabilistic prediction algorithms via betting against their predictions.
Conformal testing in a binary model situation
Conformal testing is a way of testing the IID assumption based on conformal prediction.
Retrain or not retrain: Conformal test martingales for change-point detection
We argue for supplementing the process of training a prediction algorithm by setting up a scheme for detecting the moment when the distribution of the data changes and the algorithm needs to be retrained.
Testing for concept shift online
This note continues study of exchangeability martingales, i. e., processes that are martingales under any exchangeable distribution for the observations.
Training conformal predictors
Efficiency criteria for conformal prediction, such as \emph{observed fuzziness} (i. e., the sum of p-values associated with false labels), are commonly used to \emph{evaluate} the performance of given conformal predictors.
Cross-conformal e-prediction
This note discusses a simple modification of cross-conformal prediction inspired by recent work on e-values.
Computationally efficient versions of conformal predictive distributions
Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather than set predictions.
Testing randomness
This paper reviews known methods of testing the two hypotheses concentrating on the online mode of testing, when the observations arrive sequentially.
Conformal calibrators
Most existing examples of full conformal predictive systems, split-conformal predictive systems, and cross-conformal predictive systems impose severe restrictions on the adaptation of predictive distributions to the test object at hand.
The origins and legacy of Kolmogorov's Grundbegriffe
April 25, 2003, marked the 100th anniversary of the birth of Andrei Nikolaevich Kolmogorov, the twentieth century's foremost contributor to the mathematical and philosophical foundations of probability.
History and Overview Probability 60-03 (Primary) 01A60, 60A05 (Secondary)
Conformal predictive distributions with kernels
This paper reviews the checkered history of predictive distributions in statistics and discusses two developments, one from recent literature and the other new.
Universally consistent predictive distributions
This paper describes simple universally consistent procedures of probability forecasting that satisfy a natural property of small-sample validity, under the assumption that the observations are produced independently in the IID fashion.
Inductive Conformal Martingales for Change-Point Detection
We consider the problem of quickest change-point detection in data streams.
Universal probability-free prediction
We construct universal prediction systems in the spirit of Popper's falsifiability and Kolmogorov complexity and randomness.
Criteria of efficiency for conformal prediction
We study optimal conformity measures for various criteria of efficiency of classification in an idealised setting.
Large-scale probabilistic predictors with and without guarantees of validity
This paper studies theoretically and empirically a method of turning machine-learning algorithms into probabilistic predictors that automatically enjoys a property of validity (perfect calibration) and is computationally efficient.
The fundamental nature of the log loss function
The standard loss functions used in the literature on probabilistic prediction are the log loss function, the Brier loss function, and the spherical loss function; however, any computable proper loss function can be used for comparison of prediction algorithms.
Prediction with Advice of Unknown Number of Experts
In the framework of prediction with expert advice, we consider a recently introduced kind of regret bounds: the bounds that depend on the effective instead of nominal number of experts.
From conformal to probabilistic prediction
This paper proposes a new method of probabilistic prediction, which is based on conformal prediction.
Efficiency of conformalized ridge regression
Conformal prediction is a method of producing prediction sets that can be applied on top of a wide range of prediction algorithms.
Regression Conformal Prediction with Nearest Neighbours
A comparison of the predictive regions produced by our measures with those of the typical regression measure suggests that a major improvement in terms of predictive region tightness is achieved by the new measures.
Combining p-values via averaging
An old result by R\"uschendorf and, independently, Meng implies that the p-values can be combined by scaling up their arithmetic mean by a factor of 2 (and no smaller factor is sufficient in general).
Statistics Theory Statistics Theory 62G10, 62F03
Venn-Abers predictors
This paper continues study, both theoretical and empirical, of the method of Venn prediction, concentrating on binary prediction problems.
Hedging predictions in machine learning
Recent advances in machine learning make it possible to design efficient prediction algorithms for data sets with huge numbers of parameters.
